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Podcast: Machine Learning Guide
Episode:

MLG 033 Transformers
Category: Technology
Duration: 00:42:14
Publish Date: 2025-02-09 01:33:00
Description:

Try a walking desk while studying ML or working on your projects! https://ocdevel.com/walk

Show notes: https://ocdevel.com/mlg/33

3Blue1Brown videos: https://3blue1brown.com/

  • Background & Motivation:

    • RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware.
    • Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability.

  • Core Architecture:

    • Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped in residual connections and layer normalization.
    • Positional Encodings: Since self-attention is permutation invariant, add sinusoidal or learned positional embeddings to inject sequence order.

  • Self-Attention Mechanism:

    • Q, K, V Explained:
      • Query (Q): The representation of the token seeking contextual info.
      • Key (K): The representation of tokens being compared against.
      • Value (V): The information to be aggregated based on the attention scores.
    • Multi-Head Attention: Splits Q, K, V into multiple “heads” to capture diverse relationships and nuances across different subspaces.
    • Dot-Product & Scaling: Computes similarity between Q and K (scaled to avoid large gradients), then applies softmax to weigh V accordingly.

  • Masking:

    • Causal Masking: In autoregressive models, prevents a token from “seeing” future tokens, ensuring proper generation.
    • Padding Masks: Ignore padded (non-informative) parts of sequences to maintain meaningful attention distributions.

  • Feed-Forward Networks (MLPs):

    • Transformation & Storage: Post-attention MLPs apply non-linear transformations; many argue they’re where the “facts” or learned knowledge really get stored.
    • Depth & Expressivity: Their layered nature deepens the model’s capacity to represent complex patterns.

  • Residual Connections & Normalization:

    • Residual Links: Crucial for gradient flow in deep architectures, preventing vanishing/exploding gradients.
    • Layer Normalization: Stabilizes training by normalizing across features, enhancing convergence.

  • Scalability & Efficiency Considerations:

    • Parallelization Advantage: Entire architecture is designed to exploit modern parallel hardware, a huge win over RNNs.
    • Complexity Trade-offs: Self-attention’s quadratic complexity with sequence length remains a challenge; spurred innovations like sparse or linearized attention.

  • Training Paradigms & Emergent Properties:

    • Pretraining & Fine-Tuning: Massive self-supervised pretraining on diverse data, followed by task-specific fine-tuning, is the norm.
    • Emergent Behavior: With scale comes abilities like in-context learning and few-shot adaptation, aspects that are still being unpacked.

  • Interpretability & Knowledge Distribution:

    • Distributed Representation: “Facts” aren’t stored in a single layer but are embedded throughout both attention heads and MLP layers.
    • Debate on Attention: While some see attention weights as interpretable, a growing view is that real “knowledge” is diffused across the network’s parameters.
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